South West scientists use Twitter analysis to predict mood

Machine learning specialists at the University of Bristol can now differentiate collective patterns in anger, sadness, and fatigue based on tweets

26th December 2017

Researchers from the University of Bristol have discovered a new way to analyse the collective mood of thousands using their Twitter interactions.

Following an analysis of 800 million anonymous tweets, the scientists were able to track not only how moods change depending on the time of day but specifically what the collective moods were – such as anger, sadness and fatigue.

“We hope that this study will encourage others to use social media to help in our understanding of the brain and mental health disorders”

Tracking patterns in the use of words relating to these emotions across our circadian rhythms (our bodies reactions to day/night and light/dark) they found strong patterns of positive and negative moods over the 24-hour day, as well as when comparing weekends to weekdays and across the seasons.

Trending on Twitter:graphs displaying different moods
across our circadian rhythms

The study was carried out as part of the universities EU-funded ThinkBIG project which aims to better explore artificial intelligence (AI) in the field of digital humanities.

Dr Fabon Dzogang (pictured left), research associate in the Department of Computer Science, says: “Our research revealed strong circadian patterns for both positive and negative moods. The profiles of anger and fatigue were found to be remarkably stable across the seasons or between the weekdays and weekends.

“The patterns that our research revealed for the positive emotions and sadness showed more variability in response to these changing conditions and higher levels of interaction with the onset of sunlight exposure. The techniques that we demonstrated on social media provide valuable tools for the study of our emotions and for the understanding of their interaction within the circadian rhythm.”

Stafford Lightman, Professor of Medicine and co-author of the research paper, adds: “Since many mental health disorders are affected by circadian rhythms, we hope that this study will encourage others to use social media to help in our understanding of the brain and mental health disorders.”

Involved in the tech community since her first meetup back in 2013, Alice came on board as TechSPARK's Assistant Editor in 2014 and was promoted to Editor in 2018. She loves a good tech conference and has covered both local and international events over the years. She is also a sucker for anything that uses tech to do good in the world.
In her spare time she can usually be found exploring the great outdoors or taking part in some sort of ridiculous sporting challenge.